What made your DDPG implementation on your environment work?
Below are some tweaks that helped me accelerate the training of DDPG on a Reacher-like environment:


Reducing the neural network size, compared to the original paper. Instead of: 



  2 hidden layers with 400 and 300 units respectively 


I used 128 units for both hidden layers. I see in your implementation that you used 256, maybe you could try reducing this. 


As suggested in the paper, I added batch normalization:



  ... to manually scale the features so they are in similar ranges across
  environments and units. We address this issue by adapting a recent technique from deep learning
  called batch normalization (Ioffe &amp; Szegedy, 2015). This technique normalizes each dimension
  across the samples in a minibatch to have unit mean and variance.


The implementation you used does not seem to include this.


Reducing the value of $\sigma$, which is a parameter of the Ornstein-Uhlenbeck process used to enable exploration. Originally, it was $0.2$, I used $0.05$. (I can't find where this parameter is set in your code.)


I am not entirely sure if this will help in your environment, but it was just to give you some ideas.

PS: Here is a link to the code I followed to build DDPG and here is a plot of rewards per episode.